Agent Observability Is Not Logging: How to Detect Autonomous System Divergence in Real Time

This article argues that traditional logging is insufficient for monitoring autonomous AI agents. It introduces the concept of 'trajectory observability,' which correlates prompts, tool calls, shell commands, identity changes, network requests, and policy decisions against a versioned task contract. The goal is to detect sequence-level divergence in real time and invoke controls before harmful actions complete. It covers combining distributed tracing, security telemetry, and a trajectory state store to identify behavioral inconsistencies like boundary exploration or lateral movement. The article is a technical deep dive into a new observability paradigm for AI agents, relevant to IT/technology professionals working with autonomous systems, security, and software engineering.